TAO Toolkit Google colab Error

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• Hardware (T4/V100/Xavier/Nano/etc) Google Colab
• Network Type ALL

When run the tao toolkit colab its showings followings error of the section Setup the environment necessary to run the TAO Networks by running the bash script

Using target directory: /usr/local
Extracting, please wait…

Unpacking finished successfully
/content/drive/MyDrive/nvidia-tao/tensorflow/setup_env.sh: 32: python3.6: not found
/content/drive/MyDrive/nvidia-tao/tensorflow/setup_env.sh: 33: python3.6: not found
/content/drive/MyDrive/nvidia-tao/tensorflow/setup_env.sh: 34: python3.6: not found
/content/drive/MyDrive/nvidia-tao/tensorflow/setup_env.sh: 37: python3.6: not found

Since this shell script is not running successfully, following
error comings for all the models.

Converting the training set to TFRecords.
/bin/bash: line 1: tao: command not found

Please guide me how to run TAO toolkit in google colab

1 Like

Unfortunately, the colab is updated to Ubuntu22.04 which does not allow to use fallback runtime. For temporary workaround, please try to run TAO on local dgpu machine or cloud machine.
Refer to Google Colab ssd.ipynb no python3.6 - #32 by Morganh

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@Morganh

As suggested, I am using ssd.ipynb in my local GPU, based on TAO quick start-> TAO Toolkit Quick Start Guide - NVIDIA Docs

Created separate conda environment with python versions 3.6.9

Downloed from this link → wget --content-disposition https://api.ngc.nvidia.com/v2/resources/nvidia/tao/tao-getting-started/versions/5.0.0/zip -O getting_started_v5.0.0.zip

When installing nvidia-tao by default its installing tao 4.0.1 version, which supposed to be installed tao 5.0.0 verison.

!pip install nvidia-tao

Collecting nvidia-tao
Using cached nvidia_tao-5.0.0-py3-none-any.whl (35 kB)
Collecting certifi>=2022.12.07
Using cached certifi-2023.7.22-py3-none-any.whl (158 kB)
Requirement already satisfied: six==1.15.0 in /opt/conda/lib/python3.6/site-packages (from nvidia-tao) (1.15.0)
Requirement already satisfied: docker==4.3.1 in /opt/conda/lib/python3.6/site-packages (from nvidia-tao) (4.3.1)
Requirement already satisfied: chardet==3.0.4 in /opt/conda/lib/python3.6/site-packages (from nvidia-tao) (3.0.4)
Requirement already satisfied: tabulate==0.8.7 in /opt/conda/lib/python3.6/site-packages (from nvidia-tao) (0.8.7)
Requirement already satisfied: websocket-client==0.57.0 in /opt/conda/lib/python3.6/site-packages (from nvidia-tao) (0.57.0)
Requirement already satisfied: idna==2.10 in /opt/conda/lib/python3.6/site-packages (from nvidia-tao) (2.10)
Collecting urllib3<2.0.0,>=1.26.15
Using cached urllib3-1.26.17-py2.py3-none-any.whl (143 kB)
Collecting nvidia-tao
Using cached nvidia_tao-4.0.1-py3-none-any.whl (155 kB)
Requirement already satisfied: requests in /opt/conda/lib/python3.6/site-packages (from nvidia-tao) (2.27.1)
Requirement already satisfied: certifi in /opt/conda/lib/python3.6/site-packages (from nvidia-tao) (2020.6.20)
Requirement already satisfied: urllib3>=1.26.5 in /opt/conda/lib/python3.6/site-packages (from nvidia-tao) (1.26.8)
Requirement already satisfied: docker-pycreds==0.4.0 in /opt/conda/lib/python3.6/site-packages (from nvidia-tao) (0.4.0)
Requirement already satisfied: charset-normalizer~=2.0.0 in /opt/conda/lib/python3.6/site-packages (from requests->nvidia-tao) (2.0.4)
Installing collected packages: nvidia-tao
Successfully installed nvidia-tao-4.0.1
WARNING: Running pip as the ‘root’ user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: 12. Virtual Environments and Packages — Python 3.11.5 documentation

!tao info
Configuration of the TAO Toolkit Instance
dockers: [‘nvidia/tao/tao-toolkit’]
format_version: 2.0
toolkit_version: 4.0.1
published_date: 03/06/2023

!python
Python 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31)
[GCC 7.3.0] on linux
Type “help”, “copyright”, “credits” or “license” for more information.

@Morganh

Above issue, It has been observed that while python 3.6.9 installed then default tao 4.0.1 will be installed, if python version upgrade to 3.7 then tao 5.0.0 is installed.

For my case i have configured new conda environment with python 3.7 so that tao 5.0.0 toolkit installed. Then i have started TAO Quick Start notebook ssd.ipynb

But i have getting following error i.e.
Converting the training set to TFRecords.
Traceback (most recent call last):
File “/opt/conda/envs/tao/bin/tao”, line 8, in
sys.exit(main())
File “/opt/conda/envs/tao/lib/python3.7/site-packages/nvidia_tao_cli/entrypoint/tao_launcher.py”, line 137, in main
args[2:]
File “/opt/conda/envs/tao/lib/python3.7/site-packages/nvidia_tao_cli/components/instance_handler/local_instance.py”, line 356, in launch_command
docker_logged_in(required_registry=task_map[task].docker_registry)
File “/opt/conda/envs/tao/lib/python3.7/site-packages/nvidia_tao_cli/components/instance_handler/utils.py”, line 151, in docker_logged_in
data = load_config_file(docker_config)
File “/opt/conda/envs/tao/lib/python3.7/site-packages/nvidia_tao_cli/components/instance_handler/utils.py”, line 86, in load_config_file
“No file found at: {}. Did you run docker login?”.format(config_path)
AssertionError: Config path must be a valid unix path. No file found at: /root/.docker/config.json. Did you run docker login?

Please also confirm is it require to install ? → bash setup/quickstart_launcher.sh --install

Thank you for that, the quick start tutorials are outdated and there are too many steps that are not working as expected.

There is no update from you for a period, assuming this is not an issue anymore. Hence we are closing this topic. If need further support, please open a new one. Thanks

Thanks for the info.
For new AssertionError, please search the error info in the forum, please create a new forum topic if needed.

This topic was automatically closed 14 days after the last reply. New replies are no longer allowed.

Note: Currently, the colab notebooks can work now on ubuntu22.04.
Please run notebooks in Running TAO Toolkit on Google Colab - NVIDIA Docs

More info can be found in GitHub - NVIDIA-AI-IOT/nvidia-tao.